During last two decades Precise Point Positioning has been considered as one of the most important methods in satellite geodesy. Despite the efforts to improve the PPP precision, this method has not yet achieved the precision of Relative Positioning methods. Most of the efforts on PPP improvement focus on the processing models and phase ambiguity. Modeling the tropospheric delay is very crucial to achieve high precision in Precise Point Positioning. There are different methods to estimate this delay such as ray-tracing or using appropriate mapping functions e.g. GMF, VMF, …, which relate the zenith path delay to slant path delay. In this paper, the ray-tracing slant path delay has been used as a reference value. Then three other delays are calculated using zenith path delay obtained from PPP and Global mapping function in different ways. The difference between the delay computed by ray-tracing method and those three other delays is applied to the RINEX observation files. The new RINEX files are implemented for PPP reprocessing. Comparing the achieved results, with the ITRF coordinates of Points, shows that applying the difference of slant path delay from ray-tracing and slant path delay which is computed by zenith path delay of PPP (using 88% of hydrostatic global mapping function and 12% of non-hydrostatic global mapping function) to the RINEX files, improve Positioning accuracy.

Tropospheric delay is always considered as one of the factors limiting the accuracy of GPS. In this paper, the three-dimensional ray tracing technique is proposed to calculate the tropospheric delay. The ability of the MODIS mission to calculate the tropospheric delay is also examined. For this purpose, an area in central Europe was selected and a MODIS acquisition on 2008/08/01 was studied. In addition, the radiosonde observations as well as ERA-Interim meteorological data were used to evaluate the obtained results. After applying corrections to the MODIS acquisition, the three-dimensional ray tracing method was implemented at the location of a GPS station using all three types of data to extract the tropospheric delay. The RMS of difference between the results of MODIS and results of radiosonde and ERA-Interim data was 1. 11 and 0. 89 cm respectively. Then, Precise Point Positioning was done using the Bernese software and tropospheric correction from MODIS, radiosonde and ERA-Interim data and compared with Precise coordinate of station. The accuracy of position with MODIS tropospheric correction is less than ones corrected with radiosonde and ERA-Interim tropospheric data. The results show the low efficiency of MODIS data for tropospheric correction of GPS observations compare to radiosonde and ERA-Interim data.

Earth's atmosphere has a series of layers, each with its own specific traits. Moving upward from ground level, these layers are named the troposphere, stratosphere, mesosphere, thermosphere and exosphere. The exosphere gradually fades away into the realm of interplanetary space. The troposphere is the lowest layer of our atmosphere.Starting at ground level, it extends upward to about 10 km above sea level. Humans live in the troposphere layer, and nearly all weather occurs in this layer and affects their activities. Ninety nine percent of the water vapor in the atmosphere is found in the troposphere; therefore most clouds appear in this layer. Air pressure and temperature drops in the troposphere with height. The tropospheric path delay is one the main error sources in Global Navigation Satellite System (GNSS) such as Global Positioning System (GPS) observations and reduces the accuracy of GNSS Point Positioning. Accurate estimation of tropospheric path delay in GNSS signals is necessary for Positioning and also its meteorological applications. The tropospheric delay is divided into the dry (hydrostatic) and wet (non-hydrostatic) parts. The dry tropospheric delay depends on the pressure variations between satellite and station on the Earth’s surface and can be determined accurately using experimental models. The wet delay can be determined by subtracting the dry delay from the total GPS derived delay. In this paper the efficiency of 3D ray tracing in increasing the accuracy of Point Positioning is investigated. The 3D ray tracing technique based on Eikonal equation is the strongest and newest ray tracing method. These equations are solved in order to get the ray path and the optical path length. The Eikonal equation itself is the solution of the so-called Helmholtz equation with respect to electro-magnetic waves. In this method the ray paths are not limited to a certain azimuthally fixed vertical plane. In 2D methods the ray paths are forced to stay within a vertical plane of constant azimuth. European Center for Medium Range Weather Forecasting (ECMWF) is currently publishing ERA-I, a global reanalysis of the meteorological data. This reanalysis provides values of several meteorological parameters on a global gride ~75 km. The vertical stratification is described on 37 pressure levels.Tropospheric corrections were calculated using 3D ray tracing, 2D ray tracing and Saastamoinen methods in Tabriz and Abarkuh stations using ERA-I meteorological parameters. These corrections were applied to the GPS observations and the stations coordinate were computed. Furthermore, these stations coordinates were determined twice using Bernese GPS processing software, one time the tropospheric delay was not canceled from observations and second time it was considered as unknown parameter and evaluated with stations coordinates. The result of this process was considered as a reference to evaluate the three prescribed correction methods. These comparisons indicate that the correction computed from 3D ray tracing is more efficient than that of 2D ray tracing and Saastamoinen model corrections. Also the correction amount in Tabriz station is meaningful with respect to Abarkuh station, which can be attributed to small variations of water vapor in Abarkuh station.

Due to advances in global navigation satellite systems, it has been possible for satellites to send different frequencies. For this reason, different combinations of these frequencies can be considered to form ionospheric codes and phase observations. In this study, the aim is to evaluate the Precise Point Positioning method using a combination of different frequencies. For this purpose, the PPPteh software provided by the authors, written under MatLab is used. PPPteh has the ability to process observations from four GPS, GLONASS, BeiDou and Galileo satellite systems to perform Precise Point Positioning. In this software, there are all possible combinations for making Dual-frequency ionosphere-free observations for all different frequencies. There are three modes for combining different frequencies for the GPS Positioning system, ten modes for the Galileo system, and three modes for building the BeiDou satellite system to make ionospheric-free observations. To evaluate the Precise Point Positioning method, four steps have been considered in terms of position accuracy and convergence time: 1) First, use the observations of two frequencies related to GPS and determine the position, 2) Combine the two systems satellite GPS and Galileo and select the best combination model, 3) Combining the two systems GPS and BeiDou and selecting the best combination and 4) Finally, after the previous three steps, the combination position will be determined using the three systems by the best frequency model and the results will be compared with each other. Based on the results provided for the Galileo and BeiDou navigation satellite systems, two combinations and were selected as the best combinations for use in determining the Precise Point Positioning, respectively. Following the Precise Point Positioning, the addition of observations on BeiDou satellites has reduced convergence time and, in most cases, increased the three-dimensional accuracy of the coordinate components. Using a combination of the signals has a better quality than the other two combinations. The same process was followed for observations of Galileo satellites, according to which the use of observations related to Galileo satellites when combined with GPS observations has increased accuracy and reduced convergence time. The use of signal signals is of better combination than the other three combinations. Finally, by combining all three systems and considering the selected frequency model in the first stage, it was concluded that the combination of three satellite navigation satellite systems GPS, Galileo and BeiDou significantly improved both in reducing convergence time and increasing the three-dimensional accuracy of the coordinates provided. Also, the error provided (the difference in the estimated coordinates with the final coordinates of the station from the IGS file), when using the Galileo and BeiDou systems in combination with the GPS, is noticeably different both in convergence and in the accuracy of the coordinates. Combining all three systems together increases accuracy and reduces convergence time. But in dual-combination with GPS, the use of Galileo satellite observations gives us higher accuracy as well as less convergence time. Therefore, choosing the right signals to form ionosphere-free observations in determining the exact absolute position as well as combining different observations with the correct weight for each signal in combination with GPS, can meet the user's needs in terms of accuracy and convergence.

The Earth’s atmosphere can be described by a model of layers. Although there are also horizontal gradients of the meteorological parameters, it is sufficient for a general model to divide the atmosphere into a vertical layer structure since the vertical gradients of the meteorological parameters are significantly larger than the horizontal ones. Furthermore due to the influence of the gravity the regional horizontal differences become smoothed out towards higher altitudes. The atmosphere can be divided into the troposphere, the stratosphere, the mesosphere, the thermosphere and the exosphere. The tropospheric path delay is one the errors in GNSS observations and reduces the accuracy of GNSS Positioning. Accurate estimation of tropospheric path delay in GNSS signals is necessary for meteorological applications. The tropospheric delay is divided into the dry and wet parts. The dry tropospheric delay depends on the pressure variations between satellite and Earth’s surface and can be determined accurately using the Saastamoinen and Hopfield models. The wet delay can be determined by subtracting the dry delay from the total GPS derived delayIn this paper the effect of radiosonde and ERA-Interim data in increasing the accuracy of Positioning is compared. European center for medium range weather forecasting (ECMWF) is currently publishing ERA-I, a global reanalysis of the data. This reanalysis provides values of several meteorological parameters on a global ∼75 km. The vertical stratification is described on 37 pressure levels. The piecewise-linear (PWL) is a simple and powerful 2D ray tracing technique which is fast and accurate in processing. The refined piecewise-linear (RPWL) technique is another 2D ray tracing technique. The 3D ray tracing technique based on Eikonal equation is the strongest and newest ray tracing method. These equations are solved in order to get the ray path and the optical path length. The Eikonal equation itself is the solution of the so-called Helmholtz equation with respect to electro-magnetic waves. In this method the ray paths are not limited to a certain azimuthally fixed vertical plane. Tropospheric corrections were calculated using both types of data using 3-D ray tracing method in Bandar Abbas and Birjand stations. The station coordinates were determined using two methods: 1-the tropospheric error was considered as unknown, 2-this error was not considered. Then tropospheric corrections obtained from ray tracing method was applied to the GPS observations and Positioning was done. The Bernese GPS software version 5.2 has been used to process the GPS data. It performs ionosphere-free linear combination equation of dual frequency GPS observations from each site within the regional network. The ZTD was calculated using this software including raw data from the GPS observation network and the ZHD was calculated according to the Saastamoinen model. The results indicate the importance of tropospheric correction in Precise Positioning. In addition, these results indicate that the results obtained using radiosonde corrections in Bandar Abbas are more accurate than the results obtained using ERA-Interim data. Although the results of two types of data in Birjand station do not have much difference. This can be attributed to small variations of water vapor and other atmospheric parameters in Birjand station.

GPS system, a satellite Positioning system with an accuracy about centimeter under special methods and conditions is used by smoothing algorithm for determining non real time location in Kinematic case. This algorithm works based on previous epoches phase observation and after the desired epoch. Since in this method more observations are used relative to real time location determining with Kalman filtering algorithm, it is expected to have good accuracy. To study this matter, both methods were tested with phase observations in Kinematic case. Comparing the variance covariance matrix of obtained unknowns showed the accuracy of the smoothing algorithm. The obtained results and the comparison are given by all epoch standard deviation graphs in this paper.

The widespread use of Global Navigation Satellite Systems (GNSS) in geodesy necessitates the transformation of coordinates from global to national systems, a process that can introduce spatial errors. This research focuses on evaluating the accuracy of coordinate transformations to the national coordinate system of Lebanon, which uses the double stereographic projection on the CLARKE 1880 ellipsoid. Specifically, the study examines the horizontal plane accuracy of this transformation process. The Lebanese map was divided into five zones for this study. Transformation parameters were generated for each zone by combining International Terrestrial Reference Frame (ITRF) 2014 coordinates (obtained using an online Precise Point Positioning service) with CLARKE 1880 coordinates calculated from known geodetic coordinates. The newly calculated parameters were then tested against the adopted parameters using 39 check Points, and positional errors in the horizontal plane were analyzed. Additionally, the inverse distance weight method was employed to interpolate transformation parameters at the boundaries between zones. The applied method significantly reduced positional errors in projected coordinates at most check Points. Furthermore, the successful application of the inverse distance weight method for interpolation proved effective in minimizing positional discrepancies when transforming coordinates in areas located along zone boundaries.

Tropospheric delay is considered as a disturbing parameter in GNSS Positioning. Therefore, it is necessary to eliminate or reduce its effects in order to increase the accuracy of position determination. However, nowadays for meteorological applications such as precipitation prediction, the amount of water vapor in troposphere is determined by obtaining the delay of GPS signal while crossing Earth’ s atmosphere because tropospheric delay is a function of pressure, temperature and humidity. Since the development of the meteorology with GPS methods in 1990s, global navigation satellite system is known as an effective way to study atmosphere, e. g. the estimation of zenith tropospheric delay. Tropospheric delay can be estimated in two ways, the double differencing technique and Precise Point Positioning technique. In Precise Point Positioning technique, one receiver is used to collect data. This method is used to directly derive zenith tropospheric delay by non-differenced observations. Today, permanent GPS networks not only are used for geodetic and surveying applications but also are used to determine the amount of water vapor in troposphere. In fact, these networks are used to predict weather condition. To do this, along with GPS receivers, meteorological sensors should be installed and functioned. These sensors are used to measure surface pressure, humidity and temperature. The collected data are used in existed equations such as Saastamoinen equation for the case of prediction. Knowing the exact amount of water vapor in the atmosphere is of great importance in predicting the weather. In this study, due to the benefits of the dual differential method, such as low costs, Precise Point Positioning (PPP) approach has been used. Also by using Precise Point Positioning (PPP) approach, Zenith Tropospheric Delay (ZTD) parameter estimated with the help of GPS receivers of SAMT system and TEHN station. Observations have been used for twenty consecutive days from 07/10/2015 to 07/29/2015. By using synoptic meteorological data, the share of the hydrostatic (dry) section from the non-hydrostatic (wet) section in Zenith Tropospheric Delay is separated. Then by using the transfer function, the delay caused by the non-hydrostatic section which is due to water vapor in the atmosphere, turned into water vapor that could rain. For processing, the three types of clock and final orbital data, rapid and ultra-rapid data produced by the International Global Navigation Satellite System (GNSS) Service (IGS) have been used. Observations was Processed in the software Bernese 5. 0. The obtained results were compared with the results of precipitation weather stations. To validate the amount of water vapor calculated by the GPS, we used the corresponding values were observed by radiosonde. For this purpose, the TEHN GPS stations and Mehrabad Airport weather station was selected. The results showed that when clock and final orbital data were used, the standard deviation, RMS and correlation were 1/2564, 1/0962 and 0/9698 Respectively, And when clock and rapid orbital data were used, these parameters were 1/5650, 1/2235 and 0/9647, respectively. Finally, The values of standard deviation, RMS and correlations for ultra-rapid data were 2/6086, 1/5796 and 0/9352, respectively.

Discussion about earthquake to reduce its casualties and damages is very important, especially in the Seismicity area like Iran that the occurrence of this natural phenomenon is seen annually. Iran has an approximate area of 1648000 square kilometers with geographical coordinates 25 to 40 degrees north latitude and 44 to 64 degrees east longitude that located in the middle of Alpine-Himalayan seismic belt. In this erea there are many active faults that their movement continues and the final balance has not been established. The occurrence of severe earthquakes as Buin Zahra earthquake (1962), Tabas (1978), Rudbar (1990), Bojnoord (1997), Bam (2003) and other numerous earthquakes prove this subject. While most natural disasters are out of human control, but it seems that Success in prediction of temporal and local of them can dramatically control damages and casualties. Earthquake occurrence in addition to changes of geometry and physics of the earth crust has many other effects. Some of its effects is in the ionosphere layer that are indicated as changes in the electrons values, ions density and electromagnetic field. Anomalies detection before earthquake is an important role for earthquake prediction. Each geophysical and geochemical parameter of the lithosphere, atmosphere and ionosphere layers that unusually changes before earthquake are known as earthquake precursor. Ionosphere changes that recognition by remote measurements (such as using Global Positioning System (GPS)) are known as earthquake ionospheric precursor.TEC (Total Electron Content) of the ionosphere can be achieved by GPS data processing. Classic methods such as mean are unable to detect non linear pattern and therefore in complex and nonlinear systems they are not suitable for recognition and prediction of time series. Because of the nonlinear behavior TEC and land surface changes in order to detect changes, in this paper an attempt is done using an artificial intelligence method including ANN (Artificial Neural Network) and multilayer Perceptron (MLP) for pattern recognition and prediction of TEC variations. Because ionospheric fluctuations usually do not have a normal distribution and do not follow Gaussian curve, in order to detect seismic anomalies, the mean and interquartile range is used to determine the lower and upper bounds. In this study several data sets from the ionospheric total electron content (TEC) derived from the GPS data processing by Bernese softwares. In this way earthquakes of Ahar located in east Azerbaijan (2012/08/11) and Bushehr (2013/4/9) have been studied and the results were compared with data from global stations. First the stations coordinates were calculated using Bernese software with PPP (Precise Point Positioning) method. Then TEC values were obtained using GIM (Global Ionosphere Model). By analyzing the causes of ionospheric anomalies such as the geomagnetic field and solar activity and remove them from the process, results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms could be useful for the prediction of nonlinear time series and outstanding anomalies ocurr some days before and after earthquake. It can be concluded that ANN algorithm has been able to detect TEC anomalies well. Also TEC values are obtained from ground stations have a high correlation with the results of global standard model.

With the development of engine technology, modern engine power has been improved a lot. Therefore more Precise analysis is of much importance. There is more emphasis put on the research on the design of cooling system. An efficient way to study heat transfer in cooling passage of an engine is CFD calculation. With CFD analysis flow pattern in coolant jacket could be analyzed. In this research the velocity, pressure and heat transfer coefficient distribution in the cooling passage of a 4-cylinder SI engine are computed via CFD code AVL Fire. The main goal of author's work is to investigate the Precise cooling. Therefore, the effects of head gasket holes on the flow distribution in the hot spot critical regions of the cylinder head can be seen. Three different schemes are proposed to enhance the flow distribution in cylinder head and finally the results are discussed.